Time-Series Database Internals : A Systems-Level Guide to Storage Engines, Compression, Indexing, Query Processing, Retention, and Scalability
Language: English
Published by Amazon Digital Services LLC - Kdp Aug 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 60.12
Quantity: 2 available
Add to basketItem description from seller
Neuware - I remember the exact moment my first production database melted down. It was our biggest launch day of the year. Our traffic surged, our dashboards froze, and our standard relational database simply gave up under the weight of a million telemetry metrics pouring in per second.I spent the next 48 hours frantically reading documentation, trying to understand why our disks were thrashing and our memory was swapping to a crawl. I realized then that standard database rules do not apply to time-series data. You aren't just writing data; you are fighting a brutal war against physics, disk I/O, and kernel memory allocation. I wrote this book so you don't have to fight that battle blindfolded. I want to give you the exact blueprint I wish I had when my cluster caught fire.What's insideWe skip the basic tutorials and dive straight into the deep end: - The OLAP Convergence: Discover how open-source giants like Apache Arrow and Parquet are tearing down the walls between data warehouses and time-series engines.- AI-Driven Internals: Witness the death of the traditional B-Tree. Learn how Machine Learning is being injected directly into databases via Learned Indexes and predictive auto-scaling.- Hardcore Benchmarking: Master the Time Series Benchmark Suite (TSBS). Learn how to generate realistic data and avoid the deadly trap of 'coordinated omission.'- OS & Kernel Tuning: Uncover why default Linux settings are quietly choking your database. I'll show you exactly how to tune swappiness, optimize XFS for append-only workloads, and unblock your TCP network buffers.- The Math Behind the Magic: Plain-English breakdowns of the complex algorithms powering your dashboards, from streaming standard deviations to PromQL's extrapolation logic.Who it's meant forThis book is written for Data Engineers, Site Reliability Engineers (SREs), Backend Developers, and Systems Architects. If you are tired of superficial documentation and want to deeply understand the hardware-to-software mechanics of the databases you operate, this is for you. Whether you are scaling a tiny startup or managing exabytes of enterprise telemetry, you will find actionable, battle-tested engineering knowledge here.Stop guessing why your queries are lagging. Stop throwing thousands of dollars at expensive cloud hardware to mask software bottlenecks. The future of data is timestamped, and the engineers who know how to wield it will build the next generation of the internet. Grab your copy today, dive into the internals, and master your data architecture before your next massive traffic spike hits.…
Seller Inventory # 9798194778553
- Title
- Time-Series Database Internals : A Systems-Level Guide to Storage Engines, Compression, Indexing, Query Processing, Retention, and Scalability
- Author
- Joseph C Gale
- Publisher
- Amazon Digital Services LLC - Kdp Aug 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798194778553
- Item weight
- 277 grams
- Dimensions
- 244x170x9 mm
I remember the exact moment my first production database melted down. It was our biggest launch day of the year. Our traffic surged, our dashboards froze, and our standard relational database simply gave up under the weight of a million telemetry metrics pouring in per second.
I spent the next 48 hours frantically reading documentation, trying to understand why our disks were thrashing and our memory was swapping to a crawl. I realized then that standard database rules do not apply to time-series data. You aren't just writing data; you are fighting a brutal war against physics, disk I/O, and kernel memory allocation. I wrote this book so you don't have to fight that battle blindfolded. I want to give you the exact blueprint I wish I had when my cluster caught fire.
What's insideWe skip the basic tutorials and dive straight into the deep end:
- The OLAP Convergence: Discover how open-source giants like Apache Arrow and Parquet are tearing down the walls between data warehouses and time-series engines.
- AI-Driven Internals: Witness the death of the traditional B-Tree. Learn how Machine Learning is being injected directly into databases via Learned Indexes and predictive auto-scaling.
- Hardcore Benchmarking: Master the Time Series Benchmark Suite (TSBS). Learn how to generate realistic data and avoid the deadly trap of "coordinated omission."
- OS & Kernel Tuning: Uncover why default Linux settings are quietly choking your database. I'll show you exactly how to tune swappiness, optimize XFS for append-only workloads, and unblock your TCP network buffers.
- The Math Behind the Magic: Plain-English breakdowns of the complex algorithms powering your dashboards, from streaming standard deviations to PromQL's extrapolation logic.
This book is written for Data Engineers, Site Reliability Engineers (SREs), Backend Developers, and Systems Architects. If you are tired of superficial documentation and want to deeply understand the hardware-to-software mechanics of the databases you operate, this is for you. Whether you are scaling a tiny startup or managing exabytes of enterprise telemetry, you will find actionable, battle-tested engineering knowledge here.
Stop guessing why your queries are lagging. Stop throwing thousands of dollars at expensive cloud hardware to mask software bottlenecks. The future of data is timestamped, and the engineers who know how to wield it will build the next generation of the internet. Grab your copy today, dive into the internals, and master your data architecture before your next massive traffic spike hits.
"Synopsis" may belong to another edition of this title.
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